Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Using smartphones for research outside clinical settings: how operating systems, app developers, and users determine geolocation data quality in mHealth studies.

Smartphones that collect user geolocation provid opportunities for mobile Health (mHealth). Although granularity of geolocation data may be high, data completeness depends on the device's operating system, application developer decisions, and user actions. We investigate completeness of geolocation data collected via smartphones of 5601 people that self-reported daily chronic pain symptoms on 349,293 days. On 17% of these days, hourly geolocation data is reported, but days with 0 (16%), 1 (14%) and 2 (13%) geolocations are common. Android phones collect geolocation more often than iPhones (median 17 versus 2 times a day). Factors on operating system level and individual user level influence completeness of geolocation data collected with smartphones. mHealth researchers should be aware of these factors when designing their studies. The mHealth research community should devise standards for reporting geolocation data quality, analysing systematic differences in data quality between participant groups, and methods for data imputation

Self-consistent estimations of heating in stacks of intrinsic Josephson junctions

We use the temperature dependent c axis critical current and characteristic gap voltage as temperature gauges to assess the self-heating in stacks of intrinsic Josephson junctions etched out from the ultrathin Bi2Sr2CaCu2O8+ δ(BSCCO) single crystals. The thermal resistance which characterizes the overall cooling efficiency is found to be ∼ 120–160 K mW-1 for the two stacks studied. This value is in agreement with earlier reports for stacks of similar geometry, while it is about two to three times higher than for simple mesas made on the surfaces of mm-size BSCCO single crystals. The suggested method can guarantee the validity of the temperature estimations inside the stacks of various geometries and different cooling efficiencies without the need for attaching additional thermometers

Self-consistent estimations of heating in stacks of intrinsic Josephson junctions

We use the temperature dependent c axis critical current and characteristic gap voltage as temperature gauges to assess the self-heating in stacks of intrinsic Josephson junctions etched out from the ultrathin Bi2Sr2CaCu2O8+ δ(BSCCO) single crystals. The thermal resistance which characterizes the overall cooling efficiency is found to be ∼ 120–160 K mW-1 for the two stacks studied. This value is in agreement with earlier reports for stacks of similar geometry, while it is about two to three times higher than for simple mesas made on the surfaces of mm-size BSCCO single crystals. The suggested method can guarantee the validity of the temperature estimations inside the stacks of various geometries and different cooling efficiencies without the need for attaching additional thermometers